An article in Monday’s New York Times examines the hypothesis that acetaminophen increases the risk of childhood asthma.
As reporter Christie Aschwanden points out, the percentage of U.S. children with asthma began to accelerate in the 1980s. That was about the same time that aspirin was found to cause Reye’s syndrome, and parents, at their doctors’ urging, turned en masse to acetaminophen to treat their children’s fevers.
The timing of those two events raised the curiosity of researchers, and, in 1998, a major immunology journal published a paper that argued that the switch to acetaminophen might explain the sudden rise in asthma cases. Many other studies supporting that theory have since been published. Earlier this month, Dr. John McBride, a pediatric pulmonologist at Akron Children’s Hospital in Ohio (and the main expert interviewed for the Times article), recommended in the journal Pediatrics that “until future studies document the safety of this drug, children with asthma or at risk for asthma should avoid the use of acetaminophen.”
McBride may be right. Giving acetaminophen to children may raise their risk of developing asthma. But he could also be wrong. For, as Aschwanden notes in her article, all but one of the studies on this topic were designed in a way that enables them to show only an association between acetaminophen and an increased risk of asthma.
Finding an association is not the same as finding a cause. Other factors — ones not yet identified in the studies — could also explain the association. “Children who take acetaminophen are usually getting it for fever control, and they get fevers because they have viral infections, which on their own are associated with developing asthma later in life,” explains a researcher in the Times article. “It’s hard to tease out whether it’s the drug or the viral infection.”
A dangerous reliance
Jonah Lehrer has a great article in Wired this month on how — and why — we often get misled when we give too much weight to these kinds of health-related correlations. All too often, such associations are found to be wrong.
“Consider the story of homocysteine, an amino acid that for several decades appeared to be linked to heart disease,” he writes. “The original paper detecting this association has been cited 1,800 times and has led doctors to prescribe various B vitamins to reduce homocysteine. However, a study published in 2010 — involving 12,064 volunteers over seven years — showed that the treatment had no effect on the risk of heart attack or stroke, despite the fact that homocysteine levels were lowered by nearly 30 percent.”
Our belief in these correlations can be dangerous, even deadly. Lehrer opens his article with one such cautionary tale: torcetrapib. In early studies, torcetrapib was found to block “good” HDL cholesterol from morphing into “bad” LDL cholesterol, and its manufacturer, Pfizer, had high hopes for it becoming another blockbuster drug for treating heart disease. But those hopes were shattered in 2006 when volunteers taking the drug in a Phase III trial were found to be 60 percent more likely to die than those taking a placebo. Apparently, the drug was raising blood pressure as well as affecting cholesterol — and the result was more, not fewer, heart attacks.
Another example: Menopausal hormone therapy. Studies showing an association between it and a lower risk of heart disease had led to its widespread use — until a carefully designed randomized trial reported in 2002 that it actually increased the risk of stroke and heart attacks (as well as breast cancer, colon cancer, gall bladder disease and dementia).
An age of diminishing returns?
In medicine, “the reliance on correlations has entered an age of diminishing returns,” writes Lehrer.
At least two major factors contribute to this trend. First, all of the easy causes have been found, which means that scientists are now forced to search for ever-subtler correlations, mining that mountain of facts for the tiniest of associations. Is that a new cause? Or just a statistical mistake? The line is getting finer; science is getting harder.
Second — and this is the biggy — searching for correlations is a terrible way of dealing with the primary subject of much modern research: those complex networks at the center of life. While correlations help us track the relationship between independent measurements, such as the link between smoking and cancer, they are much less effective at making sense of systems in which the variables cannot be isolated. Such situations require that we understand every interaction before we can reliably understand any of them. Given the byzantine nature of biology, this can often be a daunting hurdle, requiring that researchers map not only the complete cholesterol pathway but also the ways in which it is plugged into other pathways. …
Unfortunately, we often shrug off this dizzying intricacy, searching instead for the simplest of correlations.
But, as Lehrer points out, “this doesn’t mean that nothing can be known or that every causal story is equally problematic.” It just means that we must be careful of jumping to conclusions. Writes Lehrer:
Some explanations clearly work better than others, which is why, thanks largely to improvements in public health, the average lifespan in the developed world continues to increase. (According to the Centers for Disease Control and Prevention, things like clean water and improved sanitation — and not necessarily advances in medical technology — accounted for at least 25 of the more than 30 years added to the lifespan of Americans during the 20th century.) Although our reliance on statistical correlations has strict constraints — which limit modern research — those correlations have still managed to identify many essential risk factors, such as smoking and bad diets.
And yet, we must never forget that our causal beliefs are defined by their limitations. For too long, we’ve pretended that the old problem of causality can be cured by our shiny new knowledge. If only we devote more resources to research or dissect the system at a more fundamental level or search for ever more subtle correlations, we can discover how it all works. But a cause is not a fact, and it never will be; the things we can see will always be bracketed by what we cannot. And this is why, even when we know everything about everything, we’ll still be telling stories about why it happened. It’s mystery all the way down.
Lehrer’s piece is well worth the read, and it’s available in full on the Wired website.